Improvement of remote sensing multispectral image classification by using Independent Component Analysis

This paper deals with the application of Independent Component Analysis (ICA) as a solution to Blind Source Separation (BSS), in order to pre-process remote sensing multispectral images before we classify them. We analyze the structure of the considered data, and especially show that each recorded image corresponding to a spectral band may be seen as an observation consisting of a mixture (linear combination) of source images. The latter images correspond to the abundances of the pure elements (endmembers) in the pixels. Using BSS methods, one can hope to reduce the mixing effect in these observations, which then allows better recognition of the classes constituting the observed scene. Based on this approach, we create new images (i.e. at least partly separated images) by using ICA, starting from HRV SPOT images. These images are then used as inputs of a supervised classifier integrating textural information. The separated image classification results show a clear improvement compared to classification of initial images. This show the contribution of ICA as an attractive pre-processing for classification of multispectral remote sensing imagery.